Comparative analysis of different types of occlusal splints for the management of sleep bruxism: a systematic review
Bibliographic record
Abstract
BACKGROUND: Sleep bruxism is a prevalent condition in dentistry practice, characterized by involuntary grinding or clenching of the teeth during sleep. Several therapies, including occlusal splints, have been used to manage sleep bruxism and temporomandibular disorders, including occlusal splints. This study aimed to compare the effectiveness of different occlusal splints in managing sleep bruxism. METHODS: The PICO framework encompasses the characterization of the population, intervention, comparison, and pertinent outcomes. A comprehensive and systematic literature review was conducted on PubMed, Scopus, and Google Scholar to identify grey literature. The search specifically targeted scientific studies published before September 20, 2023. The Cochrane Collaboration Risk of Bias Tool assessed the accuracy of the included Randomized Control Trials (RCTs). The modified Newcastle-Ottawa Scale assessed non-randomized studies. Data were systematically extracted, synthesized, and reported thematically. RESULTS: Out of the total of 808 articles that were evaluated, only 15 articles were found to meet the specified inclusion criteria. Adjustable splints, such as full-occlusion biofeedback splints, were more effective in reducing sleep bruxism episodes, improving patient-reported symptoms, and enhancing overall well-being. The impact of different occlusal sprints on electromyographic activity varies, and potential adverse effects should be considered individually. CONCLUSIONS: This review provides valuable insights into the effectiveness of occlusal splints in managing sleep bruxism. The results of this study indicate that occlusal splint therapy is a viable treatment approach for sleep bruxism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".